To provide efficient flight training in critical environments for both commercial and military aviation, the flight simulator community is continuously improving the fidelity of the models. Radar simulations are among those which can benefit from performance improvements to increase realism, fidelity, and hence training effectiveness. This may be particularly important for the case of military Full Mission Simulators (FMS) where some crew members are dedicated to operate these sensors and analyze the data produced.
For example, Digital Radar Landmass Simulation (DRLMS) is particularly important for the air-to-ground radars and this aspect represents one of the biggest challenges to the radar simulation engineers due in part to the large size of the databases. This processing can take advantage of hardware with high computational power. With the advent of multi-core CPUs and massive parallel platforms such as GPUs, it is now possible to increase the simulation fidelity while maintaining the real-time user interactivity. But this could be guaranteed only by an efficient utilization of the hardware computation resources offered by these parallel platforms. Prior art solutions usually target a specific hardware and therefore lack flexibility.
Therefore, there is a need for an improved method and system that takes advantage of multi-core CPUs and/or massive parallel platforms for generating radar simulation images.